Systems and methods herein generally relate to automatic identification of features in images using image processor, and more particularly to automatically identifying user characteristics (such as gender, etc.) based on items users upload to image-based network storage sites.
Online Social Networks (OSNs) such as Facebook®, Twitter®, etc., have become immensely popular with three out of every four adult Internet users using at least one social networking site. Such a large scale adoption and active participation of users has led to research efforts studying relationship between users' digital behavior and their demographic attributes such as age, gender, relationship status, etc. Accurate techniques to predict these demographic attributes are useful for marketing purposes, and personalization and recommender systems.
A large scale study of Facebook® users reveals that digital records of human activity can be used to accurately predict a range of personal attributes such as age, gender, preferences, political orientation, etc. Likewise, there have been numerous works that study variations in language used in social media with age, gender, personality, etc. While most of the popular OSNs studied in literature are mostly text based, some of them (e.g., Facebook®, Twitter®) also allow people to post images and videos. Recently, OSNs such as Instagram® and Pinterest® that are majorly image based have gained popularity.